Monitoring in-production ML models at large scale using Amazon SageMaker Model Monitor

 Monitoring in-production ML models at large scale using Amazon SageMaker Model Monitor

Machine learning (ML) models are impacting business decisions of organizations around the globe, from retail and financial services to autonomous vehicles and space exploration. For these organizations, training and deploying ML models into production is only one step towards achieving business goals. Model performance may degrade over time for several reasons, such as changing consumer purchase patterns in the retail industry and changing economic conditions in the financial industry. Degrading model quality has a negative impact on business outcomes. To proactively address this problem, monitoring the performance of a deployed model is a critical process. Continuous monitoring of production models allows you to identify the right time and frequency to retrain and update the model. Although retraining too frequently can be too expensive, not retraining enough could result in less-than-optimal predictions from your model.

Amazon SageMaker is a fully managed service that enables developers and data scientists to quickly and easily build, train, and deploy ML models at any scale. After you train an ML model, you can deploy it on SageMaker endpoints that are fully managed and can serve inferences in real time with low latency. After you deploy your model, you can use Amazon SageMaker Model Monitor to continuously monitor the quality of your ML model in real time. You can also configure alerts to notify and trigger actions if any drift in model performance is observed. Early and proactive detection of these deviations enables you to take corrective actions, such as collecting new ground truth training data, retraining models, and auditing upstream systems, without having to manually monitor models or build additional tooling.

In this post, we discuss monitoring the quality of a classification model through classification metrics like accuracy, precision, and more.

Solution overview

The following diagram illustrates the high-level workflow of Model Monitor. You start with an endpoint to monitor and configure a fraction of inference data to be captured in real time and stored in an Amazon Simple Storage Service (Amazon S3) bucket of your choice. Model Monitor allows you to capture both input data sent to an endpoint and predictions made by the model. After that, you can create a baseline job to generate statistical rules and constraints that serve as the basis for your model analysis later. Then, you define monitoring job and attach it to an endpoint through a schedule.

Model Monitor starts monitoring jobs to analyze the model prediction data collected during a given period. For monitoring model performance characteristics such as accuracy or precision in real time, Model Monitor allows you to ingest the ground truth labels collected from your applic


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